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At least 235 records · Page 13Linked to original sources
PubMed1991

Bayesian diagnostic probabilities without assuming independence of symptoms.

The paper describes an application of Bayes' Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the "Simple Bayes" model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the "Simple Bayes" method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

Abdominal Pain↗
PubMed1994

Assessment of predictive models for binary outcomes: an empirical approach using operative death from cardiac surgery.

Predictive models in medical research have gained popularity among physicians as an important tool in medical decision making. Eight methodological strategies for creating predictive models are compared in a large, complex data base consisting of preoperative risk and operative outcome data on 12,712 patients undergoing coronary artery bypass grafting and entered into the Department of Veterans Affairs Cardiac Surgery Risk Assessment Program between April 1987 and March 1990. The models under consideration were developed to predict operative death (any death within 30 days following the surgical procedure or later if the result of a perioperative complication). The two strategies with the best predictive power among the eight examined were stepwise logistic regression alone and data reduction by cluster analysis combined with clinical judgement followed by a logistic regression model. The additive model based on unadjusted relative risks, the model based on Bayes' Theorem, and the logistic model using all candidate variables were good alternatives. Whether or not we imputed values did not have a significant impact on the predictive power of the models.

Aged↗
PubMed1989

[Evaluation of the diagnostic efficacy of a radioimmunologic assay: the example of thyroid tests].

Optimization of the utilization of a new radioimmunoassay (RIA) requires the identification of explicit criteria regarding when this new RIA should be used. The first step consists in the definition of the normal range on a well defined learning sample of patients. The authors describe the methods which can be used; mean and standard deviation, ROC analysis, Bayes theorem. These limits must be therefore validated on a control sample. The second step of this diagnostic evaluation must estimate the impact of the new RIA on the medical decision procedure. The historical account of thyroid function testing illustrates these different points.

Bayes Theorem↗
PubMed1993

Diagnostic value of the bronchial provocation test with methacholine in asthma. A Bayesian analysis approach.

The Bayesian analysis was used in this study to investigate the diagnostic value of the bronchial provocation test with methacholine in patients with asthma. The best cutoff value of accumulated concentration of methacholine administered that caused a 20 percent fall in FEV1 post-saline (PC20) in our sample, determined with a receiver operator characteristic curve, was 15 mg/ml. The interval security of the test was established by a pretest probability between 0.16 and 0.87 and the best test results were obtained when pretest probability was 0.48. The positive final diagnostic gain of the test was maximal at this pretest probability. We conclude that the application of Bayes' theorem, considering the pretest probability of asthma and the sensitivity and specificity of the individual PC20 obtained, increases the accuracy of the bronchial provocation test with methacholine in the diagnosis of asthma.

Adult↗
PubMedDate not supplied

Clinical evaluation of imaging tests: Part I--The basics.

Decision matrices and their derived ratios, Bayes theorem, and receiver operating characteristic curves provide simple formulas to evaluate the information content of tests. Despite their shortcomings, these tools are widely used, and they must be understood by all those who develop or use our increasingly complex and expensive technologies.

Bayes Theorem↗
PubMed1982

An improvement in computer-aided diagnosis of meningiomas after CT.

CT examination of 497 patients with subsequently proven pathology have been analysed and subjected to computer-aided diagnosis using Bayes' theorem. Our previous work was as accurate and reliable for diagnosing metastases and gliomas as that achieved by skilled radiologists in reporting. The present paper reports an alteration to the programme that has improved the diagnosis of meningioma.

Bayes Theorem↗
PubMed1987

Evaluation of clinical and screening tests on the TI-59.

A program for the Texas Instruments TI-59 with print cradle is described that allows evaluation of the results expected, according to Bayes' theorem, when applying one or a series of clinical or screening tests. The results of testing are expressed in terms of predictive values and the probability of correctly classifying a patient. The program was written primarily for instruction of medical students and health professionals.

Bayes Theorem↗
PubMedDate not supplied

Bayesian reasoning in medicine: the contributions of Lee B. Lusted, MD.

The role of Bayesian reasoning in medicine is explored from the perspective of the writings of Dr. Lee B. Lusted. Starting with the influential article by Ledley and Lusted published in Science in 1959, the contributions of Dr. Lusted and his coauthors to medical education and clinical medicine are chronicled. An explication of Bayes' theorem and its reliance on the conditional independence assumption is followed by a summary of some applications to the interpretation of diagnostic tests. Opportunities for and challenges to the widespread application of Bayesian reasoning in daily clinical medicine are described.

Bayes Theorem↗
PubMed1994

Bayesian analysis in the diagnostic process.

A diagnosis is made by a process in which the physician gathers evidence. The physician uses this evidence to put the patient in a class of patients with a disease. Bayes' theorem quantifies the effects of the evidence in increasing the probability that the patient has the disease. This article will discuss the potential of Bayesian analysis for medical diagnosis. Applications of Bayesian analysis to the diagnostic process could improve the efficiency of this process.

Appendicitis↗
PubMedDate not supplied

An analysis of the value of additional diagnostic procedures.

When several diagnostic procedures are performed on a patient, the probability of his having or not having a specific ailment will change as the result of each procedure is known. This paper describes how such a change is readily calculated using Bayes' theorem. Information theory is also used to find the increment in information from each successive test. Numerical estimates are made using data pertinent to lung X-ray examinations. Finally, some implications for diagnostic examinations in general are drawn.

Bayes Theorem↗
PubMedDate not supplied

[Importance of germ cell mosaic for genetic counseling of families with Duchenne and Becker muscular dystrophy].

Germ cell mosaics were demonstrated in Duchenne's and Becker's muscular dystrophy by molecular genetic methods. These findings affect risk estimates in these x-chromosomal muscular diseases. The mutation-selection equilibrium for x-chromosomally lethal inheritance for determining the a priori probability for risk estimate according to the Bayes theorem must therefore be redefined to take the germ cell mosaic problem into account. Taking Duchenne's and Becker's progressive muscular dystrophy as an example, the article explains how the estimation of the heterozygote risk in women seeking advice changes in different family situations.

Bayes Theorem↗
PubMed2006

Evaluation of the stability of intracortical microelectrode arrays.

In order to use recorded neural activities from the brain as control signals for neuroprosthesis devices, it is important to maintain a stable interface between chronically implanted microelectrodes and neural tissue. Our previous paper introduced a method to quantify the stability of the recording microelectrodes. In this paper, the method is refined 1) by incorporating stereotypical behavioral patterns into the spike sorting program and 2) by using a classifier based on Bayes theorem for assigning the recorded action potentials to the underlying neural generators. An improved method for calculating stability index is proposed. The results for the stability of microelectrode arrays that differ in structure are presented.

Action Potentials↗
PubMed1994

Integrated software for probabilistic identification of microorganisms.

A computer program has been designed to carry out microbial identification using probability matrices along with the Bayes theorem. When implementing the system the main considerations have been flexibility, user-friendliness and integration of identification coefficients and mathematical tests of matrices. The program has been developed in QuickBASIC for PC/MS-DOS operating systems and presents a menu-driven structure that aims to facilitate its use.

Bayes Theorem↗
PubMed1993

A probabilistic rule-based expert system.

This paper explores a medical expert system combining techniques of Bayesian network modelling with ideas of weighted inference rules. The weights of the individual rules can be estimated objectively from a training set of actual cases; and they can be used in a Monte Carlo stimulation to estimate objectively conditional probabilities of diagnosis given particular combinations of symptoms. The paper describes and evaluates a medical expert system built according to this design. The diagnostic accuracy of the program was found to be similar to that obtained through the usual application of Bayes theorem with the assumption of conditional independence of symptoms given disease, even though the Bayesian classifier has more than 70 times as many numerical parameters. The method may be promising in cases where small training sets do not permit accurate estimation of large numbers of parameters.

Abdominal Pain↗
PubMedDate not supplied

A Bayesian approach to infer geographical origins of migrants through surnames.

BACKGROUND: Surnames are an easy tool to analyse human genetic structure, mobility and evolution. Few studies use surnames to estimate human migration at different geographical level. PRIMARY OBJECTIVE: Here we propose the application of a Bayesian method to estimate the probability of geographical origin (pgo) of migrants in a given area using surnames. METHOD: This method can be applied with data recordings when they are available for at least two successive periods and in the areas which are the potential sources of emigration. The principle is that the new surnames which are arriving during the second period in the area under investigation can provide information on their geographical origins. The probability of the origin of migrants can easily be estimated iteratively from the frequency of surnames by using the Bayes' theorem. RESULTS: This method is exemplified using civil birth registers at different geographical scales. The pgo of migrants, estimated between two periods (1891-1915 and 1916-1940), (i) from French departments to Paris (ii), from these departments to Tarbes, and (iii) from counties surrounding Tarbes to Tarbes, are mapped and discussed.

Bayes Theorem↗
PubMed2005

Bayesian estimation of Karplus parameters and torsion angles from three-bond scalar couplings constants.

We apply Bayesian inference to analyze three-bond scalar coupling constants in an objective and consistent way. The Karplus curve and a Gaussian error law are used to model scalar coupling measurements. By applying Bayes' theorem, we obtain a probability distribution for all unknowns, i.e., the torsion angles, the Karplus parameters, and the standard deviation of the Gaussian. We infer all these unknowns from scalar coupling data using Markov chain Monte Carlo sampling and analytically derive a probability distribution that only involves the torsion angles.

Algorithms↗
PubMed1999

Is there a gender difference in fingerprint ridge density?

In the past it has been assumed that the fingerprints of women tend to have "fine" epidermal ridge detail while men have "coarse" ridge detail. Past studies have examined this hypothesis but have not clearly demonstrated if observed differences are statistically significant. The goal of this study is to determine if women have significantly higher ridge density, hence finer epidermal ridge detail, than men by counting ridges that occur within a well defined space. If significant gender differences do exist then the likelihood of inferring gender from given ridge densities will be explored. This study focused on 400 randomly picked ten-print cards representing 400 subjects. The demographic composition of this sample population represents 100 Caucasian males, 100 African American males, 100 Caucasian females and 100 African American females all within the age range of 18-67. Results show that women tend to have a significantly higher ridge density than men and that this trend is upheld in subjects of both Caucasian and African American descent (F = 81.96, P < 0.001). Application of Bayes' theorem suggests that a given fingerprint possessing a ridge density of 11 ridges/25 mm2 or less is most likely to be of male origin. Likewise a fingerprint having a ridge density of 12 ridges/25 mm2 or greater is most likely to be of female origin, regardless of race.

Adolescent↗
PubMed2005

Introduction to Bayesian methods II: fundamental concepts.

The use of Bayesian design and analysis is burgeoning. In this introduction to Bayesian methods, we provide basic examples of Bayesian thinking and formalism on which more complicated and comprehensive approaches are built. These include adjusting estimates using related information, the use of Bayes theorem in diagnostic testing, the relationship of the prior and posterior distributions for situations where both the data and prior distribution are Gaussian, and the key steps in a Bayesian analysis. If Bayesian methods are carefully developed and applied, they have excellent objective (i.e., frequentist) properties, providing marvelous tools to help improve the FDA regulatory process.

Bayes Theorem↗